Improved sparse channel estimation for multi-user massive MIMO systems with compressive sensing

Massive multiple input multiple output (MIMO) is a promising technology for the next generation communication system to increase data rate and throughput. To fully enhance the performance of massive MIMO and improve the quality of service, accurate channel state information (CSI) is required for coherent detection. However, due to the overwhelming pilot overhead, conventional pilot aided channel estimation (PACE) approaches are not suitable for massive MIMO systems, especially for frequency-division duplexing (FDD) systems. In this paper, we consider the channel estimation problem in FDD multiuser massive MIMO systems. A spatial correlated channel is first modeled. By exploiting the spatial correlation, the channel can be represented in a sparse form in spatial-frequency domain. Then, the theory of compressive sensing (CS) is applied to develop an effective method for channel estimation. Moreover, based on the inherent common sparsity in the user channel matrices, this paper proposes an improved sparse channel estimation orthogonal matching pursuit (OMP) algorithm to reduce the pilot overhead and improve the channel estimation accuracy. Simulation results demonstrate that the proposed algorithm can significantly reduce the pilot overhead and have the superior performance in greatly elevating the accuracy of channel estimation.